What data do I need for AI implementation?

AI typically needs: customer data (CRM), product/service information (catalogs, documentation), business processes (SOPs, workflows), historical data (past cases, decisions), and knowledge bases (policies, FAQs). The data must be: clean (no duplicates, errors), structured (consistent format), documented (metadata, definitions), and accessible (API or export).

How do I clean data for AI?

Data cleaning steps: remove duplicates, standardize formats (dates, names, addresses), fill missing values or flag them, correct obvious errors, validate against known rules, remove sensitive or PII data that shouldn't be used, and create data documentation. Budget 2-4 weeks for initial cleaning depending on data volume and condition.

Can AI work with unstructured data?

Yes, modern LLMs work well with unstructured data—PDFs, emails, chat logs, documents. However, you still need to: extract and organize the data, ensure it's searchable via RAG, chunk it appropriately for retrieval, and clean up formatting issues. Unstructured data often requires more preprocessing effort than structured data.

How Do I Prepare My Data for AI Implementation?

AI is only as good as the data it can access. Here's how to prepare your business data for AI implementation.

Data Preparation Checklist

Step	What to Do	Time Estimate
Inventory	List all data sources	1-2 days
Assess	Check quality, formats	2-5 days
Clean	Fix errors, standardize	1-4 weeks
Document	Metadata, definitions	1-2 weeks
Access	APIs, exports, security	1-2 weeks
Test	Validate with AI	1 week

What Data AI Needs

Most business AI needs:

Customer data: CRM records, contact history, preferences
Product data: Catalogs, pricing, specifications
Process data: SOPs, workflows, decision trees
Historical data: Past cases, tickets, resolutions
Knowledge base: Policies, FAQs, documentation
Unstructured data: Emails, chat logs, documents

Data Quality Requirements

AI needs data that is:

Clean: No duplicates, errors, or outdated records
Complete: Missing fields should be minimized or flagged
Consistent: Same format across sources (dates, names, etc.)
Current: Regularly updated, not stale
Documented: Clear definitions of what each field means

Cleaning Your Data

Steps to clean data for AI:

Remove duplicates: Same record in multiple places
Standardize formats: Dates (YYYY-MM-DD), names, addresses
Fix errors: Typos, wrong categorizations
Handle missing data: Fill, flag, or remove
Remove unnecessary fields: Don't include what AI doesn't need
Check for PII: Remove or mask sensitive data

Working With Unstructured Data

Documents, emails, PDFs are powerful for AI:

Extract text: OCR for PDFs, parsing for emails
Organize: Folder structure, metadata tags
Chunk appropriately: Break into retrievable sections
Index for search: Enable RAG (retrieval-augmented generation)

Access & Security

How will AI access your data?

API access: Does your CRM/database have APIs?
Exports: Can you export data regularly?
Read-only: AI should read, not modify (usually)
Access controls: Limit what AI can see
Audit logging: Track what data AI accesses

Common Data Problems

Problem	Symptom	Fix
Silos	Data in separate systems	Integration or central warehouse
Inconsistent formats	Dates as "Jan 5" and "1/5"	Standardization rules
Outdated records	Old customer info	Regular updates
Missing fields	Empty CRM entries	Validation rules, fill-in
No documentation	"What does field X mean?"	Create data dictionary

Working With Greene Solutions

We help with data preparation:

Data audit and assessment
Cleaning and standardization
Integration architecture
Security and access planning

Need help preparing your data?

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